The Wisdom of the Elders

For S.F. and I.K.

I can understand the idea behind wanting younger people, but to be honest I think older people are often more careful and responsible.

… suggests a hubris among younger workers

Totally agree.

The younger workers might know the latest cool whiz-bang stuff, but the older people will understand the big picture, and will be able to efficiently direct talent and energies. The former will know many different trees; the latter will understand the overall lay of the forest and how it all fits together.

It’s like the difference between “intelligence” and “wisdom” in Dungeons and Dragons, which I’ve mentioned in a previous post (yeah, I know that “intelligence” and “wisdom” can be hard to define, hence the scare quotes). Wisdom is not as easy as intelligence to measure. For example, it’s easy to see that somebody has the smarts to program a fancy application in the latest cool whiz-bang computing language, and not so easy to tell whether somebody is careful, responsible, and non-hubristic. So, it’s easy to give wisdom short shrift.

High intelligence and low wisdom is a dangerous combination, because it leads to hubris (I.K.’s word), which in turn leads to human-made disasters like the ones we’ve been hearing about in the news the past year or so. Someone with the opposite problem — low intelligence and high wisdom — would at least be aware of their own limitations.

I am reminded of a paper I read for my statistical consulting course, Barabba, Vincent P. (1991), Through a glass less darkly, Journal of the American Statistical Association, 86, 1-8, but only because that paper in turn referenced another work, Haeckel S.H. (1987), Presentation to the Information Steering Group, Cambridge, MA: Marketing Science Institute.

In Haeckel’s Information Hierarchy (according to Barabba — I don’t have access to Haeckel’s presentation, so I’ll have to trust Barabba on this), raw data is converted (transmuted?) into information. It takes a lot of data to produce one piece of information, in the same way that you need multiple data points to estimate a mean with a low standard error. Information in turn is converted into intelligence, which is converted into knowledge, which is finally converted into wisdom. There is attrition at each stage; e.g., in the same way that it took a lot of raw data to produce one “bit” of information, it takes a lot of information to produce one piece of intelligence, etc.

Haeckel’s Information Hierarchy (adapted from Barabba, 1991)

This is not to say that raw data is unimportant; quite the opposite, in fact, since you need raw data to even begin to ascend Haeckel’s Information Hierarchy. But what seems to be happening these days is an overreliance on the bottom rungs of the Information Hierarchy at the expense of the top.

To measure is to know.

Lord Kelvin

Measuring gives you the raw data at the bottom of Haeckel’s Information Hierarchy, which can lead to knowledge, and ultimately (one hopes) wisdom. But if wisdom itself is difficult to measure, then it will be difficult to obtain knowledge about wisdom.

Here’s a Magic The Gathering card with a delicious flavor text, one of my favorites: Counsel of the Soratami. The flavor text reads:

Wisdom is not the counting of all the drops in a waterfall.
Wisdom is learning why the water seeks the earth.

Counting all the drops of water in a waterfall would count as raw data in Haeckel’s Information Hierarchy.


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